Investigating capacity credit sensitivity to reliability metrics and computational methodologies

Journal Article · Sat Aug 20 00:00:00 EDT 2022 · Applied Energy

DOI:https://doi.org/10.1016/j.apenergy.2022.119825· OSTI ID:2418461

Muaddi, Saad ^[1]; Singh, Chanan ^[2]

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1. Texas A & M Univ., College Station, TX (United States); OSTI
2. Texas A & M Univ., College Station, TX (United States)

Assigning capacity value to renewable energy sources (RES) is a challenge faced in planning their integration with the grid. The difficulties stem from the natural characteristics of variability and intermittency of wind and solar sources. The capacity credit (CC) analysis evaluates the system’s actual power output compared with a constant capacity generator, i.e., conventional generator and determines an effective capacity to use for planning and operation. Herein this paper presents different factors that could affect the CC of a system. Two methods are proposed to determine the CC, namely equivalent firm capacity (EFC) and effective load carrying capability (ELCC). Since these methods are based on satisfying reliability criteria, daily loss of load expectation (LOLE), hourly loss of load (LOLH), and expected energy not served (EENS) have been employed as indices. To obtain the CC value, both methods apply two techniques: traditional and optimization. Genetic algorithm (GA) is the optimization approach used in this paper. Then, this work compares the two techniques and shows the superior performance of the optimization approach. Two hybrid systems, stand-alone (SA) and grid-connected (GC) modes, are proposed and used as case studies. The hybrid systems consist of photovoltaic (PV), wind turbine (WT), and battery energy storage system (BESS). In this work, three different scenarios are used to compare capacity credit: system as a whole, only wind, and no batteries. Finally, sensitivity analysis is carried out to examine the impact of varying the wind speed, solar irradiation, and load. It is demonstrated that the choice of reliability index plays an important role in determining the capacity credit and it is shown that EENS is a more comprehensive and consistent index of reliability.

• Research Organization: Washington State Univ., Pullman, WA (United States)
• Sponsoring Organization: USDOE; USDOE Office of International Affairs (IA)
• Grant/Contract Number: IA0000025
• OSTI ID: 2418461
• Alternate ID(s): OSTI ID: 1883422
• Journal Information: Applied Energy, Journal Name: Applied Energy Journal Issue: C Vol. 325; ISSN 0306-2619
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

24 POWER TRANSMISSION AND DISTRIBUTION
capacity credit
capacity value
effective load carrying capability
equivalent firm capacity
expected energy not served
hourly loss of load
hybrid PV-WT-battery system
loss of load expectation
system adequacy